class_docstr: Cropping layer for 3D data (e.g. spatial or spatio-temporal).
  
  Examples:
  
  >>> input_shape = (2, 28, 28, 10, 3)
  >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
  >>> y = keras.layers.Cropping3D(cropping=(2, 4, 2))(x)
  >>> y.shape
  (2, 24, 20, 6, 3)
  
  Args:
      cropping: Int, or tuple of 3 ints, or tuple of 3 tuples of 2 ints.
          - If int: the same symmetric cropping is applied to depth, height,
            and width.
          - If tuple of 3 ints: interpreted as three different symmetric
            cropping values for depth, height, and width:
            `(symmetric_dim1_crop, symmetric_dim2_crop, symmetric_dim3_crop)`.
          - If tuple of 3 tuples of 2 ints: interpreted as
            `((left_dim1_crop, right_dim1_crop), (left_dim2_crop,
            right_dim2_crop), (left_dim3_crop, right_dim3_crop))`.
      data_format: A string, one of `"channels_last"` (default) or
          `"channels_first"`. The ordering of the dimensions in the inputs.
          `"channels_last"` corresponds to inputs with shape
          `(batch_size, spatial_dim1, spatial_dim2, spatial_dim3, channels)`
          while `"channels_first"` corresponds to inputs with shape
          `(batch_size, channels, spatial_dim1, spatial_dim2, spatial_dim3)`.
          When unspecified, uses `image_data_format` value found in your Keras
          config file at `~/.keras/keras.json` (if exists). Defaults to
          `"channels_last"`.
  
  Input shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
        `(batch_size, first_axis_to_crop, second_axis_to_crop,
        third_axis_to_crop, channels)`
      - If `data_format` is `"channels_first"`:
        `(batch_size, channels, first_axis_to_crop, second_axis_to_crop,
        third_axis_to_crop)`
  
  Output shape:
      5D tensor with shape:
      - If `data_format` is `"channels_last"`:
        `(batch_size, first_cropped_axis, second_cropped_axis,
        third_cropped_axis, channels)`
      - If `data_format` is `"channels_first"`:
        `(batch_size, channels, first_cropped_axis, second_cropped_axis,
        third_cropped_axis)`
code: def get_config(self):
      config = {
          "cropping": self.cropping,
          "data_format": self.data_format,
      }
      base_config = super().get_config()
      return {**base_config, **config}
  
file_path: keras/keras/layers/reshaping/cropping3d.py
imports: from keras import backend
  from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
  from keras.utils import argument_validation
label_desc: Use image node_id 4 for calling the Cropping3D obj's underlying member method code with example usage: obj.get_config() and returns: dict